Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract
Background Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and discussion EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
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NLP techniques such as named entity recognition, text classification, and relation extraction are used to analyze clinical reports.
“To address this, natural language processing (NLP) techniques such as named entity recognition (NER), text classification, and relation extraction are used [38].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [38]
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Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions
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“…and tone of the conversational interface, to accomplish tasks that include identifying potential mental health conditions, determining the causes of those conditions, recognizing emotions in conversations, and understanding the relationship between environmental events and emotional responses [43].”
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“…Terminology, Healthcare Common Procedure Coding System, Logical Observation Identifiers Names and Codes, Systematized Nomenclature of Medicine, etc] were introduced in the EHRs, they still contain a vast amount of unstructured or semistructured data, including free-text clinical notes [244].”
Senthil, R., Anand, T., Somala, C. S., & Saravanan, K. M. (2024) · Future Healthcare Journal · doi:10.1016/j.fhj.2024.100182 · Cited by 82 · see citing works
“30 The Mayo Clinic utilises NLP algorithms to make essential choices regarding clinical trials based on patient data.”
MethodChallenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
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Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions
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“In patient data analysis, NLP plays a vital role in extracting and analyzing information from electronic health records (EHRs), clinical notes, pathology reports, and other healthcare documentation [63–65].”
Chen, Y., Lehmann, C. U., & Malin, B. (2024) · Journal of Medical Internet Research · doi:10.2196/60258 · Cited by 75 · see citing works
“AI significantly advances semantic interoperability—the ability of different systems to exchange data accurately with a common format and meaning [38,39].”
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
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“It enables clinicians and researchers to analyze vast amounts of text data to identify trends, predict disease outcomes, and assess treatment effectiveness [107,108].”
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“Despite AI’s recognized potential within the academic community, empirical evidence detailing its influence on clinical skills development is limited [6].”
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“Although they are still imperfect and require a skilled supervisor, they are considered acceptable when rapid image feedback is needed and local expertise is lacking [10].”
InfluentialSandu, A. S., Chifiriuc, M. C., Vrâncianu, C. O., Cristian, R.-E., ALISTAR, C. F., Constantin, M., Păun, M., Alistar, A., Popa, G. L., Popa, M. I., et al. (2025) · Infectious Diseases and Therapy · doi:10.1007/s40121-025-01143-0 · Cited by 69 · see citing works
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“NLP enables automatic extraction of valuable HIV-related information from large volumes of text, aiding early diagnosis, surveillance, and personalized patient monitoring [47,48].”
The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty
Andriollo, L., Picchi, A., Iademarco, G., Fidanza, A., Perticarini, L., Rossi, S. M. P., Logroscino, G., & Benazzo, F. M. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15010021 · Cited by 40 · see citing works
“These technologies have the potential to enhance patient education, guide clinical decision-making, and support research in orthopedic surgery [2,3].”
The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review
Milasan, L. H., & Scott, D. (2025) · International Journal of Mental Health Nursing · doi:10.1111/inm.70003 · Cited by 47 · see citing works
Chen, S., & Bai, W. (2025) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2025.1576465 · Cited by 37 · see citing works
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“In NLP, sophisticated algorithms and machine learning techniques are used to search, analyze, and interpret massive volumes of patient data and to extract valuable insights and meaningful concepts from clinical notes that were previously considered lost due to the textual nature of the data [27].”
Artificial intelligence: a new field of knowledge for nephrologists?
de Arizón, L. F., Ramírez, E. R. V., Teran, M. P., Perera-Lluna, A., de Maeztu, G., Nicolau, A., Furlano, M. M., & Torrá, R. (2023) · Clinical Kidney Journal · doi:10.1093/ckj/sfad182 · Cited by 33 · see citing works
“…from medical records; for example, diagnoses, medications and laboratory results can be extracted from electronic health records ( EHRs ) [ 10 ] in order to build diverse systems such as clinical decision support [ 11 ], patient monitoring, chatbots and virtual assistants [ 12 ], and drug…”
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“9 Initial attempts to use NLP for identifying VTE events focused on rule-based techniques such as PEFinder and MedLEE (Medical Language Extraction and Encoding System).”
Large Language Models for Health Care Text Classification: Systematic Review
Sakai, H., & Lam, S. S. (2025) · JMIR AI · doi:10.2196/79202 · Cited by 43 · see citing works
“In parallel, a few systematic reviews have explored current trends in health care text classification using NLP and machine learning techniques [31,32].”
Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)
Atmakuru, A., Shahini, A., Chakraborty, S., Seoni, S., Salvi, M., Hafeez‐Baig, A., Rashid, S., Tan, R., Barua, P. D., Molinari, F., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102673 · Cited by 30 · see citing works
Self-citationGao, Y., Wen, P., Liu, Y., Sun, Y., Qian, H., Zhang, X., Peng, H., Gao, Y., Li, C., Gu, Z., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06428-z · Cited by 29 · see citing works
“For example, based on NLP technology, AI can automatically structure medical record information and intelligently recommend examination and medication plans [28–30].”
Al Olaimat, M., Bozdag, S., & Initiative, F. T. A. D. N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae264 · Cited by 30 · see citing works
“…(RNN) models such as Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber 1997) and Gated Recurrent Unit (GRU) (Cho et al. 2014), along with Transformer (Vaswani et al. 2017) have been employed (Yadav et al. 2018, Xu et al. 2022, Yang et al. 2022, 2023, Herp et al. 2023, Hossain et al. 2023).”
MethodArtificial intelligence and headache
Stubberud, A., Helge Langseth, A., Nachev, P., Matharu, M. S., & Tronvik, E. (2024) · Cephalalgia · doi:10.1177/03331024241268290 · Cited by 28 · see citing works
“…medical imaging (3) and pathology slides (4); predictive analytics of disease progression, patient outcomes and treatment effects (5); natural language processing to extract information from clinical notes and patient records (6); drug discovery (7); and therapeutic and mechanistic inferences (8).”
Harnessing AI for enhanced evidence-based laboratory medicine (EBLM)
Pillay, T. S., Topçu, D. İ., & Yenice, S. (2025) · Clinica Chimica Acta · doi:10.1016/j.cca.2025.120181 · Cited by 31 · see citing works
A Review of Large Language Models in Healthcare: Taxonomy, Threats, Vulnerabilities, and Framework
Hamid, R., & Brohi, S. N. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110161 · Cited by 25 · see citing works
Gomes, S. M., de Carvalho, A. M., Cantalice, A. S., Magalhães, A. R., Tregidgo, D., de Oliveira, D. V. B., da Silva, E. B., Neto, E. J. D. M., da Silva-Maia, J. K., Gusmão, R. A. F., et al. (2024) · Environmental Science & Policy · doi:10.1016/j.envsci.2024.103885 · Cited by 30 · see citing works
Development and Comparative Evaluation of a Reinstructed GPT ‐4o Model Specialized in Periodontology
Fanelli, F., Saleh, M. H. A., Santamaria, P., Zhurakivska, K., Nibali, L., & Troiano, G. (2024) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.14101 · Cited by 23 · see citing works
“With further development, these tools can improve clinical decision making and documentation but still face challenges regarding generalizability and real-world reliability (Pethani and Dunn 2023; Hossain et al. 2023; Büttner et al. 2024).”
Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease
Chen, S., Loguercio, S., Chen, K., Lee, S. E., Park, J., Liu, S., Sadaei, H. J., & Torkamani, A. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00731-4 · Cited by 24 · see citing works
The artificial intelligence revolution in gastric cancer management: clinical applications
Li, R., Li, J., Wang, Y., Liu, X., Xu, W., Sun, R., Xue, B., Zhang, X., Ai, Y., Du, Y., et al. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03756-4 · Cited by 22 · see citing works
“This technology assists doctors in comprehensively evaluating the risk of gastric cancer and offers enhanced decision support during endoscopic procedures [160].”
Dai, Q., Li, M., Shi, S., Yang, M., Wang, Z., Liao, J., Li, Z., Liu, J., Deng, J. J., & Tao, L. (2025) · BMC Nursing · doi:10.1186/s12912-025-03748-9 · Cited by 25 · see citing works
“For instance, natural language processing technologies are increasingly being utilized to automate documentation processes, thereby significantly reducing the time nurses spend on paperwork [5].”
Machine Learning Techniques Applied to the Development of a Fall Risk Index for Older Adults
Millet, A., Madrid, A., Alonso-Weber, J. M., Rodríguez-Mañas, L., & Pérez‐Rodríguez, R. (2023) · IEEE Access · doi:10.1109/access.2023.3299489 · Cited by 21 · see citing works
“The data collected for this research was mainly written in natural language, maintaining a semi-structured design which is quite common in medical records as is discussed in Hossain and Soar’s work [34].”
Guleria, P., Srinivasu, P. N., & Hassaballah, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17212-w · Cited by 21 · see citing works
Săndulescu, V., Ianculescu, M., Valeanu, L., & Alexandru, A. (2024) · Algorithms · doi:10.3390/a17090376 · Cited by 21 · see citing works
Machine learning applications in healthcare clinical practice and research
Arkoudis, N., & Papadakos, S. P. (2024) · World Journal of Clinical Cases · doi:10.12998/wjcc.v13.i1.99744 · Cited by 22 · see citing works
Khalate, P., Gite, S., Pradhan, B., & Lee, C.-W. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1445204 · Cited by 23 · see citing works
Opee, S. A., Eva, A. A., Noor, A., Hasan, S. M., & Mridha, M. F. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.12122 · Cited by 20 · see citing works
“Natural language processing (NLP) tools further aid in processing unstructured clinical data and extracting useful insights for educated decision-making [28].”
A roadmap to precision medicine through post-genomic electronic medical records
Mendez, K. M., Reinke, S. N., Kelly, R. S., Chen, Q., Su, M., McGeachie, M. J., Weiss, S. T., Broadhurst, D., & Lasky-Su, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-56442-4 · Cited by 22 · see citing works
A survey on biomedical automatic text summarization with large language models
Huang, Z., Chen, X., Wang, Y., Huang, J., & Zhao, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104216 · Cited by 23 · see citing works
Omar, M., Naffaa, M. E., Glicksberg, B. S., Reuveni, H., Nadkarni, G. N., & Klang, E. (2024) · Rheumatology Advances in Practice · doi:10.1093/rap/rkae120 · Cited by 23 · see citing works
“NLP also supports the development of personalized treat-ment plans and advanced disease management, providing alternatives to the traditional one-size-fits-all treatment approaches [65].”
BackgroundTeles, A. S., de Moura, I. R., Silva, F., Roberts, A., & Stahl, D. R. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102981 · Cited by 22 · see citing works
Nag, P. K., Bhagat, A., & Priya, R. (2025) · IEEE Access · doi:10.1109/access.2025.3562131 · Cited by 24 · see citing works
“For instance, [36] highlighted the importance of addressing privacy concerns when utilizing social media data for mental health analysis.”
InfluentialQiu, X., Wang, H., Tan, X., & Jin, Y. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3527401 · Cited by 19 · see citing works
“Researchers have explored methods to fuse representations from language models with structured data, such as electronic health records and imaging data, to build more comprehensive models for tasks such as disease diagnosis and risk stratification [41].”
Artificial Intelligence in Gerontology: Data-Driven Health Management and Precision Medicine
Zhang, S., Wu, L., Zhao, Z., Fernández, J. R., & Chen, M. (2024) · Advances in Gerontology · doi:10.1134/s2079057024600691 · Cited by 18 · see citing works
The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment
Khan, M., Banerjee, S., Muskawad, S., Maity, R., Chowdhury, S. R., Ejaz, R., Kuuzie, E., & Satnarine, T. (2024) · Current Allergy and Asthma Reports · doi:10.1007/s11882-024-01152-y · Cited by 19 · see citing works
“It is extensively used for extracting clinical insights from electronic health records (EHRs) [49].”
Advancing EHR analysis: Predictive medication modeling using LLMs
Alghamdi, H. M., & Mostafa, A. (2025) · Information Systems · doi:10.1016/j.is.2025.102528 · Cited by 21 · see citing works
Berman, E., Malek, H. S., Bitzer, M., Malek, N. P., & Eickhoff, C. (2025) · Journal of Medical Internet Research · doi:10.2196/64364 · Cited by 19 · see citing works
“As the written text is used heavily in the medical domain for communication between health care providers, between health care providers and patients, and reports on diagnoses, procedures, and patient results, it is an opportune moment for LLM application [7-10].”
Alafari, F., Driss, M., & Cherif, A. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100725 · Cited by 17 · see citing works
Zendehbad, S. A., Ghasemi, J., & Khodadad, F. S. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70020 · Cited by 23 · see citing works
“Moreover, NLP technologies are designed to allow AI to interpret nonstructured data from clinical notes as well as patient interaction and pull out valuable insights to aid in diagnosis [125].”
Singh, R., Zhou, K., & Auclair, J. R. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1679611 · Cited by 18 · see citing works
“AI-enabled pharmacovigilance platforms monitor real-world data streams, including electronic health records, literature databases, and social media, to automate signal detection (26).”
Sathe, N., Deodhe, V., Sharma, Y., & Shinde, A. (2023) · International Conference on Advanced Computing & Communication Technologies (ICACCTech) · doi:10.1109/icacctech61146.2023.00108 · Cited by 18 · see citing works
A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology
Loor-Torres, R., Duran, M., Toro-Tobón, D., Chavez, M. M., Ponce, O. J., Jacome, C. S., Torres, D. S., Perneth, S. A., Montori, V. M., Golembiewski, E. H., et al. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.03.007 · Cited by 17 · see citing works
“7 The implementation of EHRs has led to exponential growth in the volume of health care data over the past 2 decades.”
Hatem, N. A. H. (2024) · Integrated Pharmacy Research and Practice · doi:10.2147/iprp.s466748 · Cited by 17 · see citing works
“21 Simultaneously, Pharmacy Informatics optimizes operational processes within pharmacy practice by leveraging technology to streamline and secure medication-related information, ensuring that AI algorithms receive relevant and current data.”
BackgroundAlgorethics in Healthcare: Balancing Innovation and Integrity in AI Development
Lastrucci, A., Pirrera, A., Lepri, G., & Giansanti, D. (2024) · Algorithms · doi:10.3390/a17100432 · Cited by 19 · see citing works
Siepmann, R. M., Baldini, G., Schmidt, C. S., Truhn, D., Müller‐Franzes, G. A., Dada, A., Kleesiek, J., Nensa, F., & Hosch, R. (2025) · Healthcare Analytics · doi:10.1016/j.health.2024.100378 · Cited by 16 · see citing works
A survey on cutting-edge relation extraction techniques based on language models
Diaz-Garcia, J. A., & Amador, J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11280-0 · Cited by 23 · see citing works
Vosoughi, P., Naghib, S. M., & Takdehghan, G. (2025) · Sensors and Actuators Reports · doi:10.1016/j.snr.2025.100385 · Cited by 19 · see citing works
Guérin, J., Nahid, A., Tassy, L., Deloger, M., Bocquet, F., Thézenas, S., Désandes, E., Deley, M. L., Durando, X., Jaffré, A., et al. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21020189 · Cited by 16 · see citing works
“Moreover, the systematic review of clinical text data in machine learning highlights the capability of NLP to unlock valuable clinical narratives for extensive analysis [27], while other studies emphasise the suitability of various deep learning algorithms for EHR applications [28].”
BackgroundPawuś, D., Porażko, T., & Paszkiel, S. (2024) · IEEE Access · doi:10.1109/access.2024.3413595 · Cited by 16 · see citing works
“Article [52] investigates the challenges and opportunities in applying NLP to extract clinical insights from Electronic Health Records (EHRs).”
BackgroundXiao, N., Huang, X., Wu, Y., Li, B., Zang, W., Shinwari, K., Тузанкина, И. А., Черешнев, В. А., & Liu, G. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1523902 · Cited by 19 · see citing works
Generative artificial intelligence for automated data extraction from unstructured medical text
Dao, N., Quesada, L., Hassan, S. M., Campo, M. I., Johnson, S. W., Ghose, S., Estépar, R. S. J., Waxman, A. B., Washko, G. R., & Rahaghi, F. N. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf097 · Cited by 15 · see citing works
How large language models can augment perioperative medicine: a daring discourse
Gabriel, R. A., Mariano, E. R., McAuley, J., & Wu, C. L. (2023) · Regional Anesthesia & Pain Medicine · doi:10.1136/rapm-2023-104637 · Cited by 15 · see citing works
Xiaoqing: A Q&A model for glaucoma based on LLMs
Xue, X., Zhang, D., Sun, C., Shi, Y., Wang, R., Tan, T., Gao, P., Fan, S., Zhai, G., Hu, M., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108399 · Cited by 18 · see citing works
The Impact of ChatGPT for Enhancing Knowledge Management in University Libraries
Rahman, M. H., & Islam, M. N. (2024) · Journal of Web Librarianship · doi:10.1080/19322909.2024.2391907 · Cited by 19 · see citing works
“The integration of advanced technologies, particularly natural language processing (NLP) models, into the realm of knowledge management, has seen increasing scholarly interest in recent years (Arnarsson et al., 2021; Hossain et al., 2023; Kang et al., 2020; Petroșanu et al., 2023).”
Sajjadi, S. M., Mohebbi, A., Ehsani, A., Marashi, A. H., Azhdarimoghaddam, A., Karami, S., Karimi, M. A., Sadeghi, M., Firoozi, K., Zamani, A. M., et al. (2025) · Abdominal Radiology · doi:10.1007/s00261-025-04810-5 · Cited by 14 · see citing works
Can ChatGPT outperform a neurosurgical trainee? a prospective comparative study.
Williams, S. C., Starup-Hansen, J., Funnell, J. P., Hanrahan, J. G., Valetopoulou, A., Singh, N., Sinha, S., Muirhead, W. R., & Marcus, H. J. (2025) · British Journal of Neurosurgery · doi:10.1080/02688697.2024.2308222 · Cited by 13 · see citing works
“24 29 Further, the use of LLMs in medical education and interview preparation is likely to expand.”
Honap, S., Danese, S., & Peyrin‐Biroulet, L. (2024) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izae131 · Cited by 13 · see citing works
“28,29 Examples of NLP in gastroenterology include a model that was able to diagnose eosino-philic esophagitis using retrospectively extracted clinical and esophagogastroduodenoscopy data, and a model that was able to identify IBD patients on thiopurines with algorithm-predicted objective remission.”
Shaheen, A. R., Afflitto, G. G., & Swaminathan, S. S. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100602 · Cited by 13 · see citing works
“12 Fink et al showed the superiority of ChatGPT and GPT-4 models in data mining and labeling tasks compared with manual annotation methods of extracting oncologic phenotypes from free-text radiographic reports for lung cancer.”
Karpagavadivu, K., Sakthivel, S., Ramya, M., Kaliappan, S., Maranan, R., & Nitheesh, S. (2024) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig64922.2024.10911601 · Cited by 13 · see citing works
“Classifying such tweets and other posts related to health can help researchers analyze the attitude of the population or identify fake news regarding health issues and other trends in such discussions [3].”
Abdalla, M., Saad, M., Abazia, D. T., Advani, A., Alhammad, A., Bernknopf, A. C., Bitra, V. R., Blommel, M. L., Conn, K. M., Goldwire, M. A., et al. (2025) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70131 · Cited by 14 · see citing works
“AI and chatbots can assist clinicians with answering routine medication questions, refill reminders, and adherence and patient monitoring [4, 44–47].”
InfluentialThe transformative role of Artificial Intelligence in genomics: Opportunities and challenges
Dara, M., Dianatpour, M., Azarpira, N., & Tanideh, N. (2025) · Gene Reports · doi:10.1016/j.genrep.2025.102314 · Cited by 11 · see citing works
de Oliveira, R. S., & Nascimento, E. G. S. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104477 · Cited by 12 · see citing works
Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes
Morales-Sánchez, R., Montalvo, S., Riaño, A., Martínez, R., & Velasco, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108830 · Cited by 11 · see citing works
Computational modeling for medical data: From data collection to knowledge discovery
Yin, Y., Xu, S., Hong, Y., Cai, Y., Tang, W., Wang, J., Shen, B., Zong, H., & Yu, G. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100079 · Cited by 11 · see citing works
Bibliometric analysis of natural language processing using CiteSpace and VOSviewer
Chen, X., Tian, W., & Fang, H. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100123 · Cited by 20 · see citing works
Guan, H., Novoa-Laurentiev, J., & Zhou, L. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104830 · Cited by 11 · see citing works
Vithanage, D., Yu, P., Xie, Q., Xu, H., Wang, L., & Deng, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111013 · Cited by 10 · see citing works
Salehin, I., Sajib, M. T. A., Badhon, N. H., Rifat, M. S. H., Amin, N., & Moon, N. N. (2025) · Engineering Reports · doi:10.1002/eng2.70365 · Cited by 9 · see citing works
“NLP is essential to the healthcare industry because it can convert vast amounts of unstructured data in electronic health records into forms that are helpful for diagnosis, treatment planning, and decision-making [82].”
A two-stream deep model for automated ICD-9 code prediction in an intensive care unit
Ayden, M. A., Yüksel, M. E., & Yüksel, S. E. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25960 · Cited by 10 · see citing works
“As a result, the advancements in deep learning techniques within the field of natural language processing have led to numerous studies in this domain [12,13].”
BackgroundSingh, A. K., Sharma, K. K., Bajpai, M. K., & Cabezuelo, A. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112374 · Cited by 10 · see citing works
Rawas, S., Tafran, C., AlSaeed, D., & Al-Ghreimil, N. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.055307 · Cited by 10 · see citing works
“A systematic review conducted by Hossain et al. [27] comprehensively examines various machine learning (ML) and deep learning (DL) techniques employed in NLP for EHRs, highlighting their effectiveness in extracting clinical insights from unstructured data.”
Xie, F., Chang, J., Luong, T. Q., Wu, B. U., Lustigova, E., Shrader, E. E., & Chen, W. (2023) · JMIR AI · doi:10.2196/51240 · Cited by 8 · see citing works
“They have been widely used for analyzing information from unstructured notes in the health care domain [18,19,37].”
MethodLakhdari, W., Bouhenna, M. M., Nacer, S. N., Dehliz, A., Benyahia, I., Bendıf, H., & Garzoli, S. (2024) · Biochemical Systematics and Ecology · doi:10.1016/j.bse.2024.104881 · Cited by 9 · see citing works
Roosan, D., Chok, J. I., Li, Y., & Khou, T. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698600 · Cited by 9 · see citing works
“This gap in research underscores the need for comprehensive studies that not only harness the capabilities of Transformer-based NLP models for SDOH extraction but also evaluates their effectiveness in real-world healthcare settings [13].”
BackgroundContrasting rule and machine learning based digital self triage systems in the USA
Naved, B. A., & Luo, Y. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01367-3 · Cited by 9 · see citing works
“If provided with feedback on the correctness of an individual patient ’ s triage, machine learning-based systems may enable more automated improvement over time 96,97 .”
Choi, A., Kim, C. H., Ryoo, J., Jeon, J., Cho, S., Lee, D., Kim, J., Lee, C., & Bae, W. (2025) · Scientific Reports · doi:10.1038/s41598-025-87161-x · Cited by 9 · see citing works
“Despite their widespread use and clear benefits, the complete utilization of DL-based NLP method in clinical settings is limited by the lack of annotated data and automated tools that are essential for effectively extracting clinical insights 15 .”
From Promise to Practice: Harnessing AI’s Power to Transform Medicine
Genovese, A., Borna, S., Gomez-Cabello, C. A., Haider, S. A., Prabha, S., Trabilsy, M., & Forte, A. J. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14041225 · Cited by 10 · see citing works
- Authors
- Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P. D., Pisani, A. R., & Turner, K.
- Affiliations
- North South University (BD)
- University of Southern Queensland (AU)
- Queensland University of Technology (AU)
- University of Rochester Medicine (US)
- Corresponding author
- Hossain, Elias
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 155
- Pages
- 106649
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4319985906
- Cited by
- 407
- References
- 161
- Citations by year
- 2026: 103 · 2025: 177 · 2024: 105 · 2023: 22
- FWCI
- 8.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- natural language processing, electronic health records, healthcare decision-making, named entity recognition, clinical information extraction, ICD-9, transfer learning, class imbalance, clinical text
- MeSH terms
- Machine Learning, Delivery of Health Care, Humans, Natural Language Processing, Information Storage and Retrieval, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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